A method for monitoring the operational status of edge-side equipment in power IoT based on association rules

The edge side of power IoT involves a large amount of equipment, including sensors, intelligent metering equipment, switches, etc. Effective management and monitoring of equipment requires effective mining for association rules, which is more difficult. To this end, a method for monitoring the operating status of edge-side equipment for power IoT based on association rules is proposed. A method for monitoring the operational status of edge-side equipment in power IoT based on association rules is designed. The data acquisition tier completes the data collection for operating status for edge-side equipment for power IoT according to the four modules for OCS, guarantee information, wave recording, and traveling wave, and finally completes the fault identification for edge-side equipment for power IoT with the immune algorithm-extreme learning machine model. Through relevant tests, it can be seen that the proposed method has ideal accuracy in detecting hidden faults for edge-side equipment for power IoT, and has higher monitoring sensitivity.